This assignment is for ETC5521 Assignment 1 by Team lorikeet comprising of Aphiaut Imuan, Xintong You, Harsh Katiyar, and Ishita Khanna.

1 Introduction and motivation

Infrastructure investment is an important part of the country. It includes federal investments such as water suppliers and private sector investments that manages electricity. It also includes the cost of research and development in technology.

Moreover, infrastructure investment plays a role in supporting both businesses and households. For example, the development of logistics in business sector has resulted in reduction of transportation cost. Households can access infrastructure services and can choose what and how to use. It also has a positive affect on both short-term and long-term economic growth. (Stupak, 2017)

As mentioned above, the case study should be of USA’s infrastructure because USA is a big country. It has large population, and people can access infrastructure services easily. Furthermore, USA also has the world’s largest economy.

2 Data description

The data comes from Bureau of Economic Analysis. The raw .xlsx file is included, or can be downloaded directly from the BEA Working paper series.

There are 3 primary data sets are already cleaned and saved as .csv files and they all have five variables.

For investment dataset

For Chain Investment

For IPD (Implicit Price Deflators)

The gdplev data set is downloaded from Bureau of Economic Analysis, which is a supplementary data set for our analysis. It records the current GDP and chain GDP in US from 1929 to 2021. Its variable contains year, GDP_current and GDP_chain whose type is all double.

For gdplev data set -

3 Questions of interest

  1. What category of investment is the highest average gross investment and Chain? Does it same?

  2. What is the trend of total basic infrastructure?

  3. what is the trend of total social infrastructure?

  4. What year and category are the highest and lowest investment and Chain?

  5. What is the relationship between total digital investment and GDP?

  6. What are the highest investment between Air, Water, Rail transportation investment ?

  7. What are the similarities/difference of trend between Federal electric power structures investment and Private electric power structures investment?

  8. How does the different type of investment change? (digital, transportation and power)

  9. What is the proportion of total social investment in all category infrastructure?

  10. What is the proportion of total basic infrastructure investment in all category infrastructure?

  11. What is the relationship between transportation investment (chained US dollars) and GDP (chained US dollars)?

  12. What are the trends in all categories infrastructure investment so far?

  13. In 2012, what is the proportion of various investments in the total investment?

  14. What category of investment has the greatest impact on GDP and how?

  15. What is the relationship between GDP and private investment, federal investment?And which one is stronger impact on GDP?

  16. What is the proportion of total digital investment in all category infrastructure?

  17. Is there a linear relationship between GDP and total basic infrastructure investment? Positive or negative?

  18. How each category is changed under Transportation from 2000 to 2017?

  19. What is the total gross investment ipd for conservation and development in each category?

  20. What is the relationship between current GDP and chained GDP?

  21. Is there any relationship between average gross investment and average GDP for each year from 1947 to 2017?

  22. Did all the group categories get equal amount of investment by the government in the last 5 years (according to the data i.e. from 2013 to 2017)?

4 Expected findings

In question 5: We expect to find the positive relationship between total digital investment and GDP. Moreover we expect to see the steep line from 1900 to 2017.

In question 11: We expect the positive relationship between transportation (chained US dollar) and GDP (chained US dollar). This expected finding can be explained by linear model and there will be high effect value to GDP.

In question 15: We expect to find a strong relationship between GDP and federal investment and there is a positive relation both sides.

In question 17: We expect to find a positive linear relationship between GDP and total basic infrastructure investment.

In Question 18. It is expected that there will be an increase in almost all category’s GDP except few which according to my assumption might have gone out of use so they are not really affecting GDP.

In Question 20. It is expected that the chained GDP will increase the same way the current GDP is increasing because chained GDP is basically the index measure production in one year relative to another.

In Question 21. We expect that there should be a relationship between GDP and Gross Investment because if government is investing more in various sectors then the government is also expecting to get more from all these sectors and that should affect the GDP directly.

In Question 22: We expect that all the group categories did not get the same amount of investment because all the group are not equally important from government’s point of view.

5 Analysis and findings

Methodology

This study uses the linear model to analyze because linear models are simple and present a mathematical equation which is easy to interpret and can make predictions. Moreover, linear regression model is a reliable predictor because it is a long established statistical procedure and the character of linear regression is easy to understand. (IBM, 2022).

QUESTION-5 What is the relationship between total digital investment and GDP?

The trend of total digital infrastructure investment from 1947 to 2017

Figure 5.1: The trend of total digital infrastructure investment from 1947 to 2017

The trend of Gross Domestic Product (GDP) from 1947 to 2017

Figure 5.2: The trend of Gross Domestic Product (GDP) from 1947 to 2017

The relationship between total digital investment and gdp

Figure 5.3: The relationship between total digital investment and gdp

term estimate std.error statistic p.value
(Intercept) 659.5959744 199.8196085 3.300957 0.0015277
digital 0.1126322 0.0029293 38.450403 0.0000000
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.95541 0.9547638 1254.559 1478.434 0 1 -606.2826 1218.565 1225.353 108600291 69 71

Figure 5.1 shows the positive trend of total digital infrastructure investment, moreover, it is a steeply increasing trend between 1990 and 2017. Although overall the trend is an increasing trend, around 1999 to 2009 are a slight fluctuation. Figure 5.3 illustrates the proportion increasing between Total digital infrastructure and GDP is 1:1.

The table of results can be expressed in the formula as:

\[ \begin{align*} {GDP} = 659595.97 + 112.63{digital} \end{align*} \]

This formula means if total digital infrastructure increases by 1 million US dollars, the Gross Domestic Product will increase by 112.63 million US dollars. This result is related to the study of Zhang et al. (2022) who investigated that the increase of digital economy will increase the GDP by around 0.78%. Furthermore, he told that the increase is due to the development of new technology such as internet and mobile phone communication. R-squared is 95.54% of the variance and it shows a nice linear model. Therefore, the result of a relationship between total digital infrastructure and GDP can be summarized by the linear model.

In Addition, this resulted same as our expected finding which was a positive relationship between total digital infrastructure and GDP. However, the trend of Total digital infrastructure has steeply increased since 1990 and GDP’s has steeply increased since 1980. It means total digital infrastructure doesn’t significantly affect GDP from 1980 to 1990.

QUESTION-11 What is the relationship between transportation investment (chained US dollars) and GDP (chained US dollars)?

The trend of transpotation investment and Gross Domestic Product (GDP) from 1947 to 2017

Figure 5.4: The trend of transpotation investment and Gross Domestic Product (GDP) from 1947 to 2017

The relationship between transportation investment and gdp

Figure 5.5: The relationship between transportation investment and gdp

term estimate std.error statistic p.value
(Intercept) -5164.8342269 1185.5828124 -4.356367 4.5e-05
Transportation 0.1333108 0.0111639 11.941235 0.0e+00
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.6739024 0.6691764 2852.588 142.5931 0 1 -664.605 1335.21 1341.998 561470681 69 71

Figure 5.4 shows the positive trend of transportation investment (chained dollar), however, it is a steeply slight fluctuation between 1968 and 1998. While Gross Domestic Product (chained dollar) is a positive trend and increases over the year. Figure 5.5 illustrates the relationship between transportation investment (chained dollar) and GDP (chained dollar), nonetheless, it is difficult to explain the relationship between both variables as linear.

The table of results can be expressed in the formula as:

\[ \begin{align*} {GDP} = -5164.83 + 0.13{Transportation} \end{align*} \]

This formula means if transportation investment increases by 1 million chained US dollars, the Gross Domestic Product will increase by 0.13 million chained US dollars. This result relates to the study of Weisbrod and Reno (2009) that argued the positive significance of the relationship between transportation investment and GDP. Moreover, transportation investment has a positive effect on economic growth (Lin, 2020). R-squared is 67.39% of the variance. Therefore, the result of the relationship between transportation investment (chained dollar) and Gross Domestic Product (chained dollar) should not summarize by a linear model or this formula has omitted variables.

This result same as our expected finding that is a positive relationship between transportation investment (chained dollar) and Gross Domestic Product (chained dollar). However, this relationship can not explain by the linear model and the coefficient is too low which is not the same as our expected finding.

QUESTION-15 What is the relationship between GDP and private investment, federal investment?And which one is stronger impact on GDP?

The trends of private, federal investment and current GDP

Figure 5.6: The trends of private, federal investment and current GDP

The relationship between investment and GDP

Figure 5.7: The relationship between investment and GDP

Figure 5.6 illustrates the current GDP has a generally increasing trend over time, and the trend of gross private investment is similar to that. While the gross federal investment has fluctuated since around 1980 Figure 9.

By observing the trends of the three variables, this study predicts the current GDP and the other two might have a linear relationship, then we use a linear model to judge whether they have a linear relationship. As a result, GDP does have a linear relationship with private investment and federal investment respectively, and private investment has a stronger impact on current GDP.

R-squared is an important statistical measure that represents the proportion of the variance for current GDP that is explained by the gross private and federal investment in the regression model. For example, Figure 5.7 shows that 98.6% of private investment can explain the current GDP, rather only 78.1% of federal investment can explain that. Obviously, there is a stronger linear relationship between GDP and private investment which is unexpected. This result relates to the study of Private investment as the engine of economic growth and social welfare that investigated the private investment can explain the variance of GDP growth rate more than public investment (Doménech & Sicilia, 2021).

Therefore, this results same as our expected finding that is the relationship between both variable and GDP is a linear model. However, the R-squared of federal investment is not as expected finding because the R-squared of private investment is much more valuable.

QUESTION-17 Is there a linear relationship between GDP and total basic infrastructure investment? Positive or negative?

term estimate std.error statistic p.value
(Intercept) -337.073 117.8240 -2.861 0.0056
gross_inv 0.059 0.0008 71.672 0.0000
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.9867 0.9866 684 5137 0 1 -563.2 1132 1139 32280928 69 71
The relationship between GDP and total basic infrastructure investment

Figure 5.8: The relationship between GDP and total basic infrastructure investment

From the result can be expressed the formula as:

\[ \begin{align*} {GDP} = -337073.45 + 59.02 {Basic} \end{align*} \]

This formula means if transportation investment increases by 1 million US dollars, the Gross Domestic Product will increase by 59.02 million US dollars. Moreover, the distribution of data has a positive linear relationship in figure 5.8. The trend is increasing because the infrastructure investment effect increases a huge stock of public capital such as developing or creating new roads. That is a direct effect to increase government spending (G) in the GDP formula (Stupak, 2017). Furthermore, Gunnion (2021) claims that investment in infrastructure has a positive impact on economic output. R-squared is 98.67% of the variance, therefore, the result of the relationship between total basic infrastructure investment and Gross Domestic Product can summarized by the linear model.

Additionally, these results can confirm the expected finding is correct by the graph, the result of regression, and R-square.

QUESTION-18 How each category is changed under Transportation from 2000 to 2017?

Change in transportation categories from 2000 to 2017

Figure 5.9: Change in transportation categories from 2000 to 2017

5.9 shows the change in each category in the form of grid from year 2000 to 2017 under Transportation. It shows that some of the category remained constant with slow or little change in the chained gross investment but Highway and streets and S&L have shown a tremendous decrease being the highest invested category of GDP (large amount of money invested in it)

QUESTION-20 What is the relationship between current GDP and chained GDP?

Relationship between Current and Chained GDP over the years

Figure 5.10: Relationship between Current and Chained GDP over the years

5.10 shows the yearly comparison between GDP in current USD and Chained GDP over the years. It is visible that in starting of the graph there is very low GDP that is around 1920s. The vertical line represents the average of Real GDP to see what is average GDP invested over the years and where the current GDP is lying (how far from the average). But I think the average is basically lined up because of very low GDP in the starting of the years as most of the points lie there.

QUESTION-21 Is there any relationship between average gross investment and average GDP for each year from 1947 to 2017?

Table 5.1: Average gross investment and GDP per year from 1947 to 2017
year Average_gross_investment Average_GDP
1947 453.3 249.6
1948 615.2 274.5
1949 715.9 272.5
1950 778.9 299.8
1951 880.6 346.9
1952 947.8 367.3
1953 1028.4 389.2
1954 1100.1 390.5
1955 1131.5 425.5
1956 1312.8 449.4
1957 1462.0 474.0
1958 1481.9 481.2
1959 1521.0 521.7
1960 1553.4 542.4
1961 1624.0 562.2
1962 1694.7 603.9
1963 1832.7 637.5
1964 1974.4 684.5
1965 2183.0 742.3
1966 2436.9 813.4
1967 2627.9 860.0
1968 2886.1 940.7
1969 2980.8 1017.6
1970 3166.4 1073.3
1971 3365.1 1164.9
1972 3566.3 1279.1
1973 3887.2 1425.4
1974 4389.3 1545.2
1975 4706.5 1684.9
1976 4955.5 1873.4
1977 5200.7 2081.8
1978 5870.3 2351.6
1979 6617.1 2627.3
1980 7397.9 2857.3
1981 7862.8 3207.0
1982 8053.1 3343.8
1983 8049.3 3634.0
1984 9060.9 4037.6
1985 10015.1 4339.0
1986 10320.8 4579.6
1987 10978.0 4855.2
1988 11230.9 5236.4
1989 11724.4 5641.6
1990 12810.2 5963.1
1991 13078.6 6158.1
1992 13533.9 6520.3
1993 14173.5 6858.6
1994 14367.6 7287.2
1995 15356.6 7639.7
1996 16371.6 8073.1
1997 17574.9 8577.6
1998 19181.4 9062.8
1999 20997.0 9631.2
2000 24223.2 10251.0
2001 25370.7 10581.9
2002 25251.9 10929.1
2003 25766.0 11456.5
2004 26219.1 12217.2
2005 28158.6 13039.2
2006 31352.2 13815.6
2007 35739.5 14474.2
2008 37736.4 14769.9
2009 36897.7 14478.1
2010 36353.0 15049.0
2011 36700.1 15599.7
2012 38801.5 16254.0
2013 38972.2 16843.2
2014 41006.7 17550.7
2015 42216.3 18206.0
2016 43589.4 18695.1
2017 45272.1 19479.6

Figure 5.11: Average GDP and Average gross investment from 1947 to 2017

From the above table 5.1 and plot 5.11, we can say that with increase in time, government is increasing overall investments on various sectors and as a result of which GDP is also increasing. This coincides with our expected result.

QUESTION-22 Did all the group categories get equal amount of investment by the government in the last 5 years (according to the data i.e. from 2013 to 2017)?

Figure 5.12: Average investment in million USD in different sectors from 2013 to 2017

We expected that all the departments are not equally important from the government’s point of view and the government invests more in more important sectors like POWER, SOCIAL, BASIC INFRASTRUCTURE, DIGITAL, etc. but surprisingly, in the above Figure 5.12 USA invests much less in NATURAL GAS/PETROLEUM POWER as expected because USA is the largest producer of oil and gas according to NASDAQ.

6 Conclusion

This study explores infrastructural investment data in the USA and includes 4 interesting questions about the relationship between different categories of infrastructural investment and GDP. This study investigates the relationship between the total digital investment and GDP as a positive linear. Moreover, the correlative implication between transportation investment (chained US dollars) and GDP (chained US dollars) is a positive linear relationship. Furthermore, each association of private investment, federal investment, and GDP is a positive linear relationship and private investment is a higher effect on GDP than federal investment. In addition, total basic infrastructure investment has a positive linear relationship with GDP. In conclusion, the investment the study includes with GDP is all positive linear relationship, but different investments have different degrees of impact on GDP.

We concluded that there was not much change in investment amounts in Transportation sector between 2000 and 2017 and also there is almost a linear relationship between chained GDP and GDP in current dollars.

We also concluded that USA started investing more in different sectors over the years and as a result we witnessed increase in the GDP of the country. Also, USA invests in most of its major sectors but still there are few major sectors in which USA invests much less as expected like Natural gas/Petroleum Power sector because USA is the largest oil and gas producer in the world.

7 References

Session information
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.2.0 (2022-04-22)
##  os       macOS 13.4
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       Australia/Melbourne
##  date     2023-06-17
##  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date (UTC) lib source
##  backports     1.4.1   2021-12-13 [2] CRAN (R 4.2.0)
##  bit           4.0.4   2020-08-04 [2] CRAN (R 4.2.0)
##  bit64         4.0.5   2020-08-30 [2] CRAN (R 4.2.0)
##  bookdown    * 0.27    2022-06-14 [2] CRAN (R 4.2.0)
##  broom       * 1.0.4   2023-03-11 [1] CRAN (R 4.2.0)
##  bslib         0.3.1   2021-10-06 [2] CRAN (R 4.2.0)
##  cellranger    1.1.0   2016-07-27 [2] CRAN (R 4.2.0)
##  class         7.3-20  2022-01-16 [2] CRAN (R 4.2.0)
##  classInt      0.4-3   2020-04-07 [2] CRAN (R 4.2.0)
##  cli           3.6.1   2023-03-23 [1] CRAN (R 4.2.0)
##  colorspace    2.0-3   2022-02-21 [2] CRAN (R 4.2.0)
##  crayon        1.5.1   2022-03-26 [2] CRAN (R 4.2.0)
##  crosstalk     1.2.0   2021-11-04 [2] CRAN (R 4.2.0)
##  curl          4.3.2   2021-06-23 [2] CRAN (R 4.2.0)
##  data.table    1.14.2  2021-09-27 [2] CRAN (R 4.2.0)
##  DBI           1.1.2   2021-12-20 [2] CRAN (R 4.2.0)
##  digest        0.6.29  2021-12-01 [2] CRAN (R 4.2.0)
##  dplyr       * 1.1.1   2023-03-22 [1] CRAN (R 4.2.0)
##  DT          * 0.27    2023-01-17 [1] CRAN (R 4.2.0)
##  e1071         1.7-9   2021-09-16 [2] CRAN (R 4.2.0)
##  ellipsis      0.3.2   2021-04-29 [2] CRAN (R 4.2.0)
##  evaluate      0.15    2022-02-18 [2] CRAN (R 4.2.0)
##  fansi         1.0.3   2022-03-24 [2] CRAN (R 4.2.0)
##  farver        2.1.0   2021-02-28 [2] CRAN (R 4.2.0)
##  fastmap       1.1.0   2021-01-25 [2] CRAN (R 4.2.0)
##  forcats     * 1.0.0   2023-01-29 [1] CRAN (R 4.2.0)
##  generics      0.1.2   2022-01-31 [2] CRAN (R 4.2.0)
##  ggplot2     * 3.4.2   2023-04-03 [1] CRAN (R 4.2.0)
##  glue          1.6.2   2022-02-24 [2] CRAN (R 4.2.0)
##  gridExtra   * 2.3     2017-09-09 [1] CRAN (R 4.2.0)
##  gtable        0.3.0   2019-03-25 [2] CRAN (R 4.2.0)
##  highr         0.9     2021-04-16 [2] CRAN (R 4.2.0)
##  hms           1.1.3   2023-03-21 [1] CRAN (R 4.2.0)
##  htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.2.0)
##  htmlwidgets   1.6.2   2023-03-17 [1] CRAN (R 4.2.0)
##  httr          1.4.5   2023-02-24 [1] CRAN (R 4.2.0)
##  jquerylib     0.1.4   2021-04-26 [2] CRAN (R 4.2.0)
##  jsonlite      1.8.4   2022-12-06 [1] CRAN (R 4.2.0)
##  kableExtra  * 1.3.4   2021-02-20 [1] CRAN (R 4.2.0)
##  KernSmooth    2.23-20 2021-05-03 [2] CRAN (R 4.2.0)
##  knitr       * 1.42    2023-01-25 [2] CRAN (R 4.2.0)
##  labeling      0.4.2   2020-10-20 [2] CRAN (R 4.2.0)
##  lattice       0.20-45 2021-09-22 [2] CRAN (R 4.2.0)
##  lazyeval      0.2.2   2019-03-15 [2] CRAN (R 4.2.0)
##  lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.2.0)
##  lubridate   * 1.9.2   2023-02-10 [1] CRAN (R 4.2.0)
##  magrittr      2.0.3   2022-03-30 [2] CRAN (R 4.2.0)
##  Matrix        1.5-4   2023-04-04 [1] CRAN (R 4.2.0)
##  mgcv          1.8-40  2022-03-29 [2] CRAN (R 4.2.0)
##  munsell       0.5.0   2018-06-12 [2] CRAN (R 4.2.0)
##  nlme          3.1-157 2022-03-25 [2] CRAN (R 4.2.0)
##  pillar        1.9.0   2023-03-22 [1] CRAN (R 4.2.0)
##  pkgconfig     2.0.3   2019-09-22 [2] CRAN (R 4.2.0)
##  plotly      * 4.10.1  2022-11-07 [1] CRAN (R 4.2.0)
##  proxy         0.4-26  2021-06-07 [2] CRAN (R 4.2.0)
##  purrr       * 1.0.1   2023-01-10 [1] CRAN (R 4.2.0)
##  R6            2.5.1   2021-08-19 [2] CRAN (R 4.2.0)
##  Rcpp          1.0.10  2023-01-22 [1] CRAN (R 4.2.0)
##  readr       * 2.1.4   2023-02-10 [1] CRAN (R 4.2.0)
##  readxl      * 1.4.2   2023-02-09 [1] CRAN (R 4.2.0)
##  rematch       1.0.1   2016-04-21 [2] CRAN (R 4.2.0)
##  rlang         1.1.0   2023-03-14 [1] CRAN (R 4.2.0)
##  rmarkdown     2.14    2022-04-25 [2] CRAN (R 4.2.0)
##  rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.2.0)
##  rvest         1.0.3   2022-08-19 [1] CRAN (R 4.2.0)
##  sass          0.4.5   2023-01-24 [1] CRAN (R 4.2.0)
##  scales        1.2.1   2022-08-20 [1] CRAN (R 4.2.0)
##  sessioninfo   1.2.2   2021-12-06 [2] CRAN (R 4.2.0)
##  sf          * 1.0-12  2023-03-19 [1] CRAN (R 4.2.0)
##  stringi       1.7.6   2021-11-29 [2] CRAN (R 4.2.0)
##  stringr     * 1.5.0   2022-12-02 [1] CRAN (R 4.2.0)
##  svglite       2.1.0   2022-02-03 [2] CRAN (R 4.2.0)
##  systemfonts   1.0.4   2022-02-11 [2] CRAN (R 4.2.0)
##  tibble      * 3.2.1   2023-03-20 [1] CRAN (R 4.2.0)
##  tidyr       * 1.3.0   2023-01-24 [1] CRAN (R 4.2.0)
##  tidyselect    1.2.0   2022-10-10 [1] CRAN (R 4.2.0)
##  tidyverse   * 2.0.0   2023-02-22 [1] CRAN (R 4.2.0)
##  timechange    0.2.0   2023-01-11 [1] CRAN (R 4.2.0)
##  tinytex     * 0.44    2023-02-01 [2] CRAN (R 4.2.0)
##  tzdb          0.3.0   2022-03-28 [2] CRAN (R 4.2.0)
##  units         0.8-0   2022-02-05 [2] CRAN (R 4.2.0)
##  utf8          1.2.2   2021-07-24 [2] CRAN (R 4.2.0)
##  vctrs         0.6.1   2023-03-22 [1] CRAN (R 4.2.0)
##  viridisLite   0.4.1   2022-08-22 [1] CRAN (R 4.2.0)
##  vroom         1.6.1   2023-01-22 [1] CRAN (R 4.2.0)
##  webshot       0.5.3   2022-04-14 [2] CRAN (R 4.2.0)
##  withr         2.5.0   2022-03-03 [2] CRAN (R 4.2.0)
##  xfun          0.38    2023-03-24 [1] CRAN (R 4.2.0)
##  xml2          1.3.3   2021-11-30 [2] CRAN (R 4.2.0)
##  yaml          2.3.5   2022-02-21 [2] CRAN (R 4.2.0)
## 
##  [1] /Users/autmini13/Library/R/arm64/4.2/library
##  [2] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
## 
## ──────────────────────────────────────────────────────────────────────────────